State-of-Charge estimation of Li-ion battery at different temperatures using particle filter

• Published in: Journal of engineering (Stevenage, England) Vol. 2019; no. 18; pp. 5320 - 5324
• Main Authors: Sangwan, Venu, Kumar, Rajesh, Kumar Rathore, Akshay
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.07.2019; Wiley
• Subjects: adaptive filters; ASMO; battery management system; battery management systems; battery parameters; battery powered vehicles; Bayes methods; BMS; electric vehicle; lithium compounds; Li‐ion battery state‐of‐charge estimation; meta-heuristic ageist spider monkey algorithm; optimisation; optimisation problem; particle filter; particle filtering (numerical methods); recursive Bayesian filtering based adaptive filter algorithms; residual service time; secondary cells; SOC estimation; temperature variation; The 7th International Conference on Renewable Power Generation (RPG 2018); temperature variation; adaptive filters; battery powered vehicles; SOC estimation; battery management system; battery parameters; particle filtering (numerical methods); BMS; Li-ion battery state-of-charge estimation; lithium compounds; ASMO; particle filter; battery management systems; optimisation problem; meta-heuristic Ageist Spider Monkey Algorithm; recursive Bayesian filtering based adaptive filter algorithms; optimisation; electric vehicle; secondary cells; Bayes methods; residual service time
• ISSN: 2051-3305; 2051-3305
• DOI: 10.1049/joe.2018.9234

State-of-Charge (SOC) estimation is one of the fundamental functions undertaken by Battery Management System (BMS) in an Electric Vehicle (EV) to assess the residual service time of the battery during operation. Thus, an accurate model of the battery that efficiently describes its dynamic characteristics is necessary for precise SOC estimation. The variation in temperature effects battery parameters, and consequently, the estimation of SOC is subject to change in temperature. In this paper, the identification of parameters of battery model is considered as an optimisation problem and solved using meta-heuristic Ageist Spider Monkey Algorithm (ASMO) under the influence of varying temperature. The developed model is used for SOC estimation using three Recursive Bayesian filtering based adaptive filter algorithms. Further, the efficiency of the implemented adaptive filter algorithms is compared in terms of solution quality and computation time required for evaluation of SOC.